DFR Split Means
Catherine Walsh
4/1/2020
We also wanted to investigate what happens when we run a similar analysis, but we split groups based on DFR accuracy.
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(ggplot2)
library(reshape2)
library(psych)##
## Attaching package: 'psych'## The following objects are masked from 'package:ggplot2':
##
## %+%, alphalibrary(patchwork)
library(rockchalk)##
## Attaching package: 'rockchalk'## The following object is masked from 'package:dplyr':
##
## summarizeload('data/load_effects_DFR.RData')
load('data/behav.RData')
load('data/structural_measures.RData')
load('data/connectivity_data.RData')
source("split_into_groups.R")
source("prep_split_for_bar_plots.R")
source("plot_bars.R")acc <- p200_data[p200_data$PTID %in% p200_indiv_ROI_DFR_delay$PTID,]
acc <- merge(acc,construct_vars_omnibus)
data_for_plot <- merge(p200_indiv_ROI_DFR_delay,acc)
p1 <- ggplot(data_for_plot, aes(x=XDFR_MRI_ACC_L3, y = DFR_ROIs))+
geom_point()+
stat_smooth(method="lm")+
geom_text(x=0.85,y=2.25,label="r = 0.25**")
p2 <- ggplot(data_for_plot, aes(x=XDFR_MRI_ACC_L3, y = DFR_L_dlPFC))+
geom_point()+
stat_smooth(method="lm")+
geom_text(x=0.9,y=1,label="r = 0.26***")
p3 <- ggplot(data_for_plot, aes(x=XDFR_MRI_ACC_L3, y = DFR_L_IPS))+
geom_point()+
stat_smooth(method="lm")+
geom_text(x=0.85,y=.9,label="r = 0.24**")
p4 <- ggplot(data_for_plot, aes(x=XDFR_MRI_ACC_L3, y = DFR_L_preSMA))+
geom_point()+
stat_smooth(method="lm")+
geom_text(x=0.9,y=1,label="r = 0.28***")
(p1+p2)/(p3+p4)
#sort by omnibus span
p200_data_sorted <- acc[order(acc$XDFR_MRI_ACC_L3),]
low_DFR <- p200_data_sorted[1:56,]
med_DFR <- p200_data_sorted[58:113,]
high_DFR <-p200_data_sorted[115:170,]
low_DFR$level <- "low"
med_DFR$level <- "med"
high_DFR$level <- "high"
things_to_hist <- rbind(low_DFR,med_DFR,high_DFR)
DFR_groups <- list(high=data.frame(high_DFR),med=data.frame(med_DFR), low=data.frame(low_DFR), all=data.frame(things_to_hist))
save(list=c("DFR_groups", "p200_data_sorted"),file="data/DFR_split_groups_info.RData")low_med_split_DFR <- p200_data_sorted[1:85,]
high_med_split_DFR <- p200_data_sorted[86:170,]
low_med_split_DFR$level <- "low_median"
high_med_split_DFR$level <- "high_median"Compare how group IDs change
load("~/Documents/Code/RDoC_for_GitHub/data/split_WM_groups_fMRI.RData")
DFR_med_split <- rbind(low_med_split_DFR,high_med_split_DFR)
DFR_split_data <- dplyr::select(DFR_groups[["all"]],"PTID","XDFR_MRI_ACC_L3","level")
WM_split_data <- dplyr::select(split_constructs[["all"]],"PTID","omnibus_span_no_DFR_MRI","level")
DFR_med_split_data <- dplyr::select(DFR_med_split,"PTID","level")
colnames(DFR_split_data)[3] <- "DFR_level"
colnames(WM_split_data)[3] <- "WM_level"
colnames(DFR_med_split_data)[2] <- "DFR_med_level"
comparison <- merge(DFR_split_data,WM_split_data,by="PTID")
comparison <- merge(comparison, DFR_med_split_data,by="PTID")
comparison <- dplyr::select(comparison,"PTID", "omnibus_span_no_DFR_MRI","XDFR_MRI_ACC_L3", "WM_level","DFR_level","DFR_med_level")
comparison_counts <- data.frame(matrix(nrow=3,ncol=3))
colnames(comparison_counts) <- c("low_WM","med_WM","high_WM")
rownames(comparison_counts) <- c("low_DFR","med_DFR","high_DFR")
for (row in seq.int(1,3)){
for (col in seq.int(1,3)){
comparison_counts[row,col] <- sum(substr(comparison$WM_level,1,3)==substr(colnames(comparison_counts)[row],1,3) & substr(comparison$DFR_level,1,3)==substr(colnames(comparison_counts)[col],1,3))
}
}
comparison_counts_melt <- data.frame(matrix(ncol=3,nrow=9))
colnames(comparison_counts_melt) <- c("WM","DFR","count")
row_count <- 1
for (row in seq.int(1,3)){
for (col in seq.int(1,3)){
comparison_counts_melt$WM[row_count] <- colnames(comparison_counts)[row]
comparison_counts_melt$DFR[row_count] <- rownames(comparison_counts)[col]
comparison_counts_melt$count[row_count] <- comparison_counts[row,col]
row_count = row_count+1
}
}
comparison_counts_melt$WM <- factor(comparison_counts_melt$WM,levels = c("low_WM","med_WM","high_WM"))
comparison_counts_melt$DFR <- factor(comparison_counts_melt$DFR,levels=c("low_DFR","med_DFR","high_DFR"))Subjects split on DFR high load accuracy. 56 subjects per group. Approximately 50% of subjects stay in their initial group.
ggplot(data=comparison_counts_melt)+
geom_tile(aes(x=WM,y=DFR, fill=count))+
geom_text(aes(x=WM,y=DFR,label=count),size=8,color="white")+
theme(legend.position = "none") 
comparison_counts_median <- data.frame(matrix(nrow=2,ncol=3))
colnames(comparison_counts_median) <- c("low_WM","med_WM","high_WM")
rownames(comparison_counts_median) <- c("low_DFR_median","high_DFR_median")
comparison_counts_median[1,1] <- sum(comparison$WM_level == "low" & comparison$DFR_med_level == "low_median")
comparison_counts_median[2,1] <- sum(comparison$WM_level == "low" & comparison$DFR_med_level == "high_median")
comparison_counts_median[1,2] <- sum(comparison$WM_level == "med" & comparison$DFR_med_level == "low_median")
comparison_counts_median[2,2] <- sum(comparison$WM_level == "med" & comparison$DFR_med_level == "high_median")
comparison_counts_median[1,3] <- sum(comparison$WM_level == "high" & comparison$DFR_med_level == "low_median")
comparison_counts_median[2,3] <- sum(comparison$WM_level == "high" & comparison$DFR_med_level == "high_median")
comparison_counts_median_melt <- data.frame(matrix(ncol=3,nrow=6))
colnames(comparison_counts_median_melt) <- c("WM","DFR_median","count")
row_count <- 1
for (row in seq.int(1,2)){
for (col in seq.int(1,3)){
comparison_counts_median_melt$WM[row_count] <- colnames(comparison_counts_median)[col]
comparison_counts_median_melt$DFR_median[row_count] <- rownames(comparison_counts_median)[row]
comparison_counts_median_melt$count[row_count] <- comparison_counts_median[row,col]
row_count = row_count+1
}
}
comparison_counts_median_melt$WM <- factor(comparison_counts_median_melt$WM,levels = c("low_WM","med_WM","high_WM"))
comparison_counts_median_melt$DFR <- factor(comparison_counts_median_melt$DFR_median,levels=c("low_DFR_median","high_DFR_median"))Later, we’re going to make our lives easier by just looking at DFR split on median (high scorers and low scorers), so let’s look at this as well.
We tend to see that if you have at least medium capacity, you tend to do well on the task.
ggplot(data=comparison_counts_median_melt)+
geom_tile(aes(x=WM,y=DFR_median, fill=count))+
geom_text(aes(x=WM,y=DFR_median,label=count),size=8,color="white")
split_constructs <- split_into_groups(constructs_fMRI[1:7],DFR_groups)
split_clinical <- split_into_groups(p200_clinical_zscores, DFR_groups)
split_DFR_delay <- split_into_groups(p200_indiv_ROI_DFR_delay, DFR_groups)
split_DFR_cue <- split_into_groups(p200_indiv_ROI_DFR_cue, DFR_groups)
split_DFR_probe <- split_into_groups(p200_indiv_ROI_DFR_probe, DFR_groups)
split_DFR_FFA <- split_into_groups(p200_FFA,DFR_groups)
split_DFR_HPC_Ant <- split_into_groups(p200_HPC_Ant, DFR_groups)
split_DFR_HPC_Med <- split_into_groups(p200_HPC_Med, DFR_groups)
split_DFR_HPC_Post <- split_into_groups(p200_HPC_Post, DFR_groups)
split_fullMask_delay <- split_into_groups(p200_DFR_full_mask, DFR_groups)
split_cue_ROIs <- split_into_groups(p200_indiv_ROI_delayDFR_cuePeriod, DFR_groups)
split_demographics <- split_into_groups(p200_demographics,DFR_groups)
split_cortical_thickness_DFR <- split_into_groups(p200_DFR_fullMask_cortical_thickness,DFR_groups)
split_RS <- split_into_groups(p200_all_RS,DFR_groups)
split_beta_conn_cue <- split_into_groups(p200_beta_conn_cue,DFR_groups)
split_beta_conn_delay <- split_into_groups(p200_beta_conn_delay,DFR_groups)
split_BCT <- split_into_groups(p200_BCT_forCorr,DFR_groups)
split_indiv_partic_coeff <- split_into_groups(p200_indiv_network_ParticCoeff,DFR_groups)save(list=c("split_constructs","split_clinical","split_DFR_delay", "split_DFR_cue", "split_DFR_probe", "split_DFR_FFA", "split_DFR_HPC_Ant", "split_DFR_HPC_Med", "split_DFR_HPC_Post", "split_fullMask_delay", "split_cue_ROIs", "split_demographics","split_cortical_thickness_DFR","split_RS","split_beta_conn_cue","split_beta_conn_delay","split_BCT", "split_indiv_partic_coeff"), file="data/split_DFR_groups_fMRI.RData")split_means_demo <- data.frame(matrix(nrow=length(split_demographics)-1,ncol=8))
colnames(split_means_demo) <- c("Trio","Prisma","CS","NCS","female","male","age","age_se")
rownames(split_means_demo) <- names(split_demographics)[1:length(names(split_demographics))-1]
for (level in seq.int(1,length(split_demographics)-1)){
split_means_demo$Trio[level] <- length(split_demographics[[level]]$SCANNER[split_demographics[[level]]$SCANNER==1])
split_means_demo$Prisma[level] <- length(split_demographics[[level]]$SCANNER[split_demographics[[level]]$SCANNER==2])
split_means_demo$CS[level] <- length(split_demographics[[level]]$GROUP[split_demographics[[level]]$GROUP==1])
split_means_demo$NCS[level] <- length(split_demographics[[level]]$GROUP[split_demographics[[level]]$GROUP==2])
split_means_demo$female[level] <- length(split_demographics[[level]]$GENDER[split_demographics[[level]]$GENDER==2])
split_means_demo$male[level] <- length(split_demographics[[level]]$GENDER[split_demographics[[level]]$GENDER==1])
split_means_demo$age[level] <- mean(split_demographics[[level]]$AGE,na.rm=TRUE)
split_means_demo$age_se[level] <- sd(split_demographics[[level]]$AGE,na.rm=TRUE)/sqrt(length(split_demographics[[level]]$AGE[!is.na(split_demographics[[level]]$AGE)]))
}
split_means_demo$level <- as.factor(c("high", "med","low"))
means_melt_demo <- melt(split_means_demo,id.vars="level")Demographics
Most notable thing here is that there is a larger proportion of CS than NCS in the low performing group. Also seeing that there are more females in the mid performing group.
age_plot <- ggplot(data=split_means_demo,aes(x=level,y=age))+
geom_bar(stat="identity",width = .5, color = "#667Ea4", fill = "#667Ea4")+
geom_errorbar(aes(ymin=age-age_se,ymax=age+age_se),width=.2)+
ggtitle("Age") +
ylab("Mean +/- SE") +
scale_x_discrete(limits = c("low","med","high")) +
theme(aspect.ratio = 1)
scanner_data <- demo_plot_data.m[demo_plot_data.m$variable=="scanner_count",c(1,2,5,6)]
scanner_data$value <- scanner_data$value/56*100
scanner_plot <- ggplot(scanner_data,aes(x=level,y=value,fill=scanner))+
geom_bar(stat="identity") +
ylab("Percent (%)") +
theme(aspect.ratio=1) +
scale_x_discrete(limits = c("low","med","high")) +
ggtitle("Scanner")
gender_data <- demo_plot_data.m[demo_plot_data.m$variable=="gender_count",c(1,3,5,6)]
gender_data$value <- gender_data$value/56*100
gender_plot <- ggplot(gender_data,aes(x=level,y=value, fill=gender))+
geom_bar(stat="identity") +
ylab("Percent (%)") +
theme(aspect.ratio=1) +
scale_x_discrete(limits = c("low","med","high")) +
ggtitle("Gender")
care_data <- demo_plot_data.m[demo_plot_data.m$variable=="care_count",c(1,4:6)]
care_data$value <- care_data$value/56*100
care_plot <- ggplot(care_data,aes(x=level,y=value, fill=care))+
geom_bar(stat="identity") +
ylab("Percent (%)") +
theme(aspect.ratio=1) +
scale_x_discrete(limits = c("low","med","high")) +
ggtitle("CS vs NCS")
(age_plot + gender_plot)/(care_plot + scanner_plot)+
plot_annotation(title="Demographics split by DFR performance")
melt_constructs <- prep_split_for_bar_plots(DFR_groups)
melt_clinical <- prep_split_for_bar_plots(split_clinical)
melt_DFR_delay <- prep_split_for_bar_plots(split_DFR_delay)
melt_DFR_cue <- prep_split_for_bar_plots(split_DFR_cue)
melt_DFR_probe <- prep_split_for_bar_plots(split_DFR_probe)
melt_DFR_FFA <- prep_split_for_bar_plots(split_DFR_FFA)
melt_DFR_HPC_Ant <- prep_split_for_bar_plots(split_DFR_HPC_Ant)
melt_DFR_HPC_Med <- prep_split_for_bar_plots(split_DFR_HPC_Med)
melt_DFR_HPC_Post <- prep_split_for_bar_plots(split_DFR_HPC_Post)
melt_fullMask_delay <- prep_split_for_bar_plots(split_fullMask_delay)
melt_cue_ROIs <- prep_split_for_bar_plots(split_cue_ROIs)
melt_cortical_thickness_DFR <- prep_split_for_bar_plots(split_cortical_thickness_DFR)
melt_RS <- prep_split_for_bar_plots(split_RS)
melt_beta_conn_cue <- prep_split_for_bar_plots(split_beta_conn_cue)
melt_beta_conn_delay <- prep_split_for_bar_plots(split_beta_conn_delay)
melt_BCT <- prep_split_for_bar_plots(split_BCT)
melt_indiv_partic_coeff <- prep_split_for_bar_plots(split_indiv_partic_coeff)constructs_plots <- plot_bars(melt_constructs)
clinical_plots <- plot_bars(melt_clinical)
DFR_delay_plots <- plot_bars(melt_DFR_delay)
DFR_cue_plots <- plot_bars(melt_DFR_cue)
DFR_probe_plots <- plot_bars(melt_DFR_probe)
DFR_FFA_plots <- plot_bars(melt_DFR_FFA)
DFR_HPC_Ant_plots <- plot_bars(melt_DFR_HPC_Ant)
DFR_HPC_Med_plots <- plot_bars(melt_DFR_HPC_Med)
DFR_HPC_Post_plots <- plot_bars(melt_DFR_HPC_Post)
fullMask_delay_plots <- plot_bars(melt_fullMask_delay)
cue_ROIs_plots <- plot_bars(melt_cue_ROIs)
cortical_thickness_plots <- plot_bars(melt_cortical_thickness_DFR)
RS_plots <- plot_bars(melt_RS)
beta_conn_cue_plots <- plot_bars(melt_beta_conn_cue)
beta_conn_delay_plots <- plot_bars(melt_beta_conn_delay)
BCT_plots <- plot_bars(melt_BCT)
indiv_partic_coeff_plots <- plot_bars(melt_indiv_partic_coeff)Constructs
A nice sanity check here as well - if a subject has higher capacity and higher intelligence, they tend to have higher performance. The main statistically significant differences here are in omnibus span, where high > low.
constructs_plots[["omnibus_span_no_DFR_MRI"]]$labels$title = "Omnibus Span"
(constructs_plots[["omnibus_span_no_DFR_MRI"]]+constructs_plots[["intelligence"]]+constructs_plots[["LTM"]]) +
plot_annotation(title="Constructs split on DFR performance")
print("Omnibus Span")## [1] "Omnibus Span"span.aov <- aov(omnibus_span_no_DFR_MRI ~ level, data=split_constructs[["all"]])
summary(span.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 2.84 1.4193 5.096 0.00712 **
## Residuals 165 45.95 0.2785
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(span.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = omnibus_span_no_DFR_MRI ~ level, data = split_constructs[["all"]])
##
## $level
## diff lwr upr p adj
## med-high -0.1305318 -0.3664026 0.10533899 0.3922824
## low-high -0.3167678 -0.5526386 -0.08089705 0.0050411
## low-med -0.1862360 -0.4221068 0.04963473 0.1514683print("LTM")## [1] "LTM"LTM.aov <- aov(LTM ~ level, data=split_constructs[["all"]])
summary(LTM.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 3.12 1.5605 2.822 0.0624 .
## Residuals 161 89.02 0.5529
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 4 observations deleted due to missingnessTukeyHSD(LTM.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = LTM ~ level, data = split_constructs[["all"]])
##
## $level
## diff lwr upr p adj
## med-high -0.1549342 -0.4903700 0.180501649 0.5200105
## low-high -0.3381273 -0.6751126 -0.001142138 0.0490211
## low-med -0.1831932 -0.5201784 0.153792056 0.4051248print("Intelligence")## [1] "Intelligence"intelligence.aov <- aov(intelligence ~ level, data=split_constructs[["all"]])
summary(intelligence.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.17 0.5867 0.999 0.37
## Residuals 165 96.87 0.5871TukeyHSD(intelligence.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = intelligence ~ level, data = split_constructs[["all"]])
##
## $level
## diff lwr upr p adj
## med-high -0.02305072 -0.3655146 0.3194132 0.9861279
## low-high -0.18768753 -0.5301514 0.1547763 0.3993499
## low-med -0.16463682 -0.5071007 0.1778271 0.4927506Clinical
There is a significant negative correlation between BPRS and performance - this doesn’t come through quite as well in the split groups, but we definitely still see the pattern and there is a trend in differences in the ANOVA.
ggplot(data=data_for_plot, aes(x=XDFR_MRI_ACC_L3, y = WHO_ST_S32))+
geom_point()+
stat_smooth(method="lm")+
geom_text(x=0.9,y=55,label="r=-0.13")+
ggtitle("Correlation of WHODAS and DFR Performance")
print("WHODAS")## [1] "WHODAS"print(cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$WHO_ST_S32))##
## Pearson's product-moment correlation
##
## data: data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$WHO_ST_S32
## t = -1.6545, df = 168, p-value = 0.0999
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.27194790 0.02436222
## sample estimates:
## cor
## -0.1266163ggplot(data=data_for_plot, aes(x=XDFR_MRI_ACC_L3, y = BPRS_TOT))+
geom_point()+
stat_smooth(method="lm")+
geom_text(x=.9,y=70,label="r=-0.19*")+
ggtitle("Correlation of BPRS and DFR Performance")
print("BPRS")## [1] "BPRS"print(cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$BPRS_TOT))##
## Pearson's product-moment correlation
##
## data: data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$BPRS_TOT
## t = -2.5715, df = 168, p-value = 0.01099
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.33529800 -0.04542107
## sample estimates:
## cor
## -0.1946049clinical_plots[["WHO_ST_S32"]]$labels$title <- "WHODAS"
clinical_plots[["BPRS"]]$labels$title <- "BPRS"
(clinical_plots[["WHO_ST_S32"]] + clinical_plots[["BPRS_TOT"]])+
plot_annotation(title="Clinical measures split by DFR performance")
print("WHODAS")## [1] "WHODAS"WHODAS.aov <- aov(WHO_ST_S32 ~ level, data=split_clinical[["all"]])
summary(WHODAS.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 3.61 1.8040 1.857 0.159
## Residuals 165 160.32 0.9717print("BPRS")## [1] "BPRS"BPRS.aov <- aov(BPRS_TOT ~ level, data=split_clinical[["all"]])
summary(BPRS.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 5.45 2.7239 2.844 0.0611 .
## Residuals 165 158.04 0.9578
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Cue Period
Full mask
There is a linear relationship between performance and load effect in the cue mask during the delay period.
fullMask_delay_plots[["cue_low"]]+fullMask_delay_plots[["cue_high"]]+fullMask_delay_plots[["cue_loadEffect"]]+
plot_annotation(title="BOLD signal from full delay period mask during cue period")
print("Load Effect")## [1] "Load Effect"cue_LE.aov <- aov(cue_loadEffect ~ level, data=split_fullMask_delay[["all"]])
summary(cue_LE.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.944 0.4722 3.082 0.0485 *
## Residuals 165 25.284 0.1532
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(cue_LE.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = cue_loadEffect ~ level, data = split_fullMask_delay[["all"]])
##
## $level
## diff lwr upr p adj
## med-high -0.10768817 -0.2826502 0.067273864 0.3150380
## low-high -0.18268512 -0.3576472 -0.007723088 0.0384648
## low-med -0.07499695 -0.2499590 0.099965080 0.5692292Scatter Plots
Let’s look at some scatter plots for the more linear looking things - particularly the high load and the load effect.
data_for_plot <- merge(p200_data,p200_DFR_full_mask,by="PTID")
ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=cue_high))+
geom_point()+
stat_smooth(method="lm")+
geom_text(x=0.9,y=2,label="r=0.25**")+
ggtitle("High Load")
print("High Load")## [1] "High Load"cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$cue_high)##
## Pearson's product-moment correlation
##
## data: data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$cue_high
## t = 3.32, df = 168, p-value = 0.001104
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1014029 0.3842921
## sample estimates:
## cor
## 0.2481301ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=cue_loadEffect))+
geom_point()+
stat_smooth(method="lm")+
geom_text(x=0.9,y=1.5,label="r=0.24**")+
ggtitle("Load Effect")
print("Load Effect")## [1] "Load Effect"cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$cue_loadEffect)##
## Pearson's product-moment correlation
##
## data: data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$cue_loadEffect
## t = 3.2502, df = 168, p-value = 0.001393
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.09623747 0.37983718
## sample estimates:
## cor
## 0.2432285Individual ROIs
The only significant thing here is the occipital regions - more decrease in
DFR_cue_plots[["L_FEF_low"]] + DFR_cue_plots[["L_FEF_high"]] + DFR_cue_plots[["L_FEF_loadEffect"]]
DFR_cue_plots[["L_insula_low"]] + DFR_cue_plots[["L_insula_high"]] + DFR_cue_plots[["L_insula_loadEffect"]]
DFR_cue_plots[["L_IPS_low"]] + DFR_cue_plots[["L_IPS_high"]] + DFR_cue_plots[["L_IPS_loadEffect"]]
DFR_cue_plots[["L_occipital_low"]] + DFR_cue_plots[["L_occipital_high"]] + DFR_cue_plots[["L_occipital_loadEffect"]]
DFR_cue_plots[["R_FEF_low"]] + DFR_cue_plots[["R_FEF_high"]] + DFR_cue_plots[["R_FEF_loadEffect"]]
DFR_cue_plots[["R_insula_low"]] + DFR_cue_plots[["R_insula_high"]] + DFR_cue_plots[["R_insula_loadEffect"]]
DFR_cue_plots[["R_IPS_low"]] + DFR_cue_plots[["R_IPS_high"]] + DFR_cue_plots[["R_IPS_loadEffect"]]
DFR_cue_plots[["R_MFG_low"]] + DFR_cue_plots[["R_MFG_high"]] + DFR_cue_plots[["R_MFG_loadEffect"]]
DFR_cue_plots[["R_preSMA_low"]] + DFR_cue_plots[["R_preSMA_high"]] + DFR_cue_plots[["R_preSMA_loadEffect"]]
DFR_cue_plots[["R_occipital_low"]] + DFR_cue_plots[["R_occipital_high"]] + DFR_cue_plots[["R_occipital_loadEffect"]]
print("L FEF")## [1] "L FEF"cue_L_FEF.aov <- aov(L_FEF_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_L_FEF.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.55 0.2764 1.227 0.296
## Residuals 165 37.17 0.2253print("L insula")## [1] "L insula"cue_L_insula.aov <- aov(L_insula_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_L_insula.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.88 0.4398 1.676 0.19
## Residuals 165 43.29 0.2624print("L IPS")## [1] "L IPS"cue_L_IPS.aov <- aov(L_IPS_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_L_IPS.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.91 0.9541 2.295 0.104
## Residuals 165 68.60 0.4157print("L occipital")## [1] "L occipital"cue_L_occipital.aov <- aov(L_occipital_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_L_occipital.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 3.30 1.648 3.59 0.0298 *
## Residuals 165 75.74 0.459
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print("R FEF")## [1] "R FEF"cue_R_FEF.aov <- aov(R_FEF_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_R_FEF.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 2.08 1.0387 1.907 0.152
## Residuals 165 89.87 0.5447print("R insula")## [1] "R insula"cue_R_insula.aov <- aov(R_insula_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_R_insula.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.44 0.2221 1.007 0.367
## Residuals 165 36.39 0.2205print("R IPS")## [1] "R IPS"cue_R_IPS.aov <- aov(R_IPS_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_R_IPS.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.85 0.9259 2.374 0.0962 .
## Residuals 165 64.34 0.3899
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print("R MFG")## [1] "R MFG"cue_R_MFG.aov <- aov(R_MFG_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_R_MFG.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.597 0.2987 1.594 0.206
## Residuals 165 30.910 0.1873print("R occipital")## [1] "R occipital"cue_R_occipital.aov <- aov(R_occipital_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_R_occipital.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 3.81 1.9029 4.48 0.0127 *
## Residuals 165 70.09 0.4248
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print("R preSMA")## [1] "R preSMA"cue_R_preSMA.aov <- aov(R_preSMA_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_R_preSMA.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.71 0.3540 0.759 0.47
## Residuals 165 76.97 0.4665Delay Period
Full Mask
There are significant differnces in the high load and load effect - the high load trials have differences: high > low and high > medium, while the load effect only has high > low.
fullMask_delay_plots[["delay_low"]]+fullMask_delay_plots[["delay_high"]]+fullMask_delay_plots[["delay_loadEffect"]]+
plot_annotation(title="BOLD signal from full delay period mask during delay period")
print("Low Load")## [1] "Low Load"delay_L1.aov <- aov(delay_low ~ level, data=split_fullMask_delay[["all"]])
summary(delay_L1.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.051 0.02533 0.833 0.437
## Residuals 165 5.018 0.03042TukeyHSD(delay_L1.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = delay_low ~ level, data = split_fullMask_delay[["all"]])
##
## $level
## diff lwr upr p adj
## med-high -0.01471531 -0.09266343 0.06323281 0.8960257
## low-high 0.02720635 -0.05074177 0.10515447 0.6877751
## low-med 0.04192166 -0.03602646 0.11986978 0.4130273print("High Load")## [1] "High Load"delay_L3.aov <- aov(delay_high ~ level, data=split_fullMask_delay[["all"]])
summary(delay_L3.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.501 0.25048 7.329 0.000892 ***
## Residuals 165 5.639 0.03417
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(delay_L3.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = delay_high ~ level, data = split_fullMask_delay[["all"]])
##
## $level
## diff lwr upr p adj
## med-high -0.118340890 -0.20096672 -0.03571506 0.0025291
## low-high -0.113164799 -0.19579063 -0.03053897 0.0041193
## low-med 0.005176091 -0.07744974 0.08780192 0.9879721print("Load Effect")## [1] "Load Effect"delay_LE.aov <- aov(delay_loadEffect ~ level, data=split_fullMask_delay[["all"]])
summary(delay_LE.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.593 0.29673 6.214 0.0025 **
## Residuals 165 7.880 0.04776
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(delay_LE.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = delay_loadEffect ~ level, data = split_fullMask_delay[["all"]])
##
## $level
## diff lwr upr p adj
## med-high -0.10362558 -0.2012982 -0.005952923 0.0347174
## low-high -0.14037115 -0.2380438 -0.042698489 0.0024325
## low-med -0.03674557 -0.1344182 0.060927091 0.6475245Scatter Plots
Let’s look at some scatter plots for the more linear looking things - particularly the high load and the load effect.
data_for_plot <- merge(p200_data,p200_DFR_full_mask,by="PTID")
ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=delay_high))+
geom_point()+
stat_smooth(method="lm")+
geom_text(x=0.9,y=0.6,label="r=0.26***")+
ggtitle("High Load")
print("High Load")## [1] "High Load"cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$delay_high)##
## Pearson's product-moment correlation
##
## data: data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$delay_high
## t = 3.5615, df = 168, p-value = 0.00048
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1191920 0.3995341
## sample estimates:
## cor
## 0.264953ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=delay_loadEffect))+
geom_point()+
stat_smooth(method="lm")+
geom_text(x=0.9,y=1,label="r=0.26***")+
ggtitle("Load Effect")
print("Load Effect")## [1] "Load Effect"cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$delay_loadEffect)##
## Pearson's product-moment correlation
##
## data: data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$delay_loadEffect
## t = 3.4403, df = 168, p-value = 0.0007331
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1102834 0.3919204
## sample estimates:
## cor
## 0.2565393Individual ROIs
No L dMFG, all show high > low, except for . L aMFG, L dlPFC, R dlPFC also showed high > med, and R medial parietal only showed high > med.
(DFR_delay_plots[["DFR_L_aMFG"]] + DFR_delay_plots[["DFR_L_dlPFC"]] + DFR_delay_plots[["DFR_L_dMFG"]]) + plot_annotation(title="individual DFR delay period ROIs")
(DFR_delay_plots[["DFR_L_IPS"]] + DFR_delay_plots[["DFR_L_preSMA"]] + DFR_delay_plots[["DFR_R_dlPFC"]]) 
(DFR_delay_plots[["DFR_R_dMFG"]] + DFR_delay_plots[["DFR_R_IPS"]] + DFR_delay_plots[["DFR_R_medParietal"]]) 
print("L aMFG")## [1] "L aMFG"L_aMFG.aov <- aov(DFR_L_aMFG ~ level, data=split_DFR_delay[["all"]])
summary(L_aMFG.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.431 0.7155 9.074 0.000182 ***
## Residuals 165 13.011 0.0789
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(L_aMFG.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = DFR_L_aMFG ~ level, data = split_DFR_delay[["all"]])
##
## $level
## diff lwr upr p adj
## med-high -0.14467585 -0.2701834 -0.01916829 0.0193402
## low-high -0.22277889 -0.3482864 -0.09727133 0.0001295
## low-med -0.07810304 -0.2036106 0.04740451 0.3071420print("L dlPFC")## [1] "L dlPFC"L_dlPFC.aov <- aov(DFR_L_dlPFC ~ level, data=split_DFR_delay[["all"]])
summary(L_dlPFC.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.976 0.4878 7.024 0.00118 **
## Residuals 165 11.459 0.0694
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(L_dlPFC.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = DFR_L_dlPFC ~ level, data = split_DFR_delay[["all"]])
##
## $level
## diff lwr upr p adj
## med-high -0.12027344 -0.2380607 -0.002486185 0.0441621
## low-high -0.18376405 -0.3015513 -0.065976794 0.0008848
## low-med -0.06349061 -0.1812779 0.054296649 0.4113955print("L dMFG")## [1] "L dMFG"L_dMFG.aov <- aov(DFR_L_dMFG ~ level, data=split_DFR_delay[["all"]])
summary(L_dMFG.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.315 0.15733 2.883 0.0588 .
## Residuals 165 9.005 0.05458
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print("L IPS")## [1] "L IPS"L_IPS.aov <- aov(DFR_L_IPS ~ level, data=split_DFR_delay[["all"]])
summary(L_IPS.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.572 0.28619 5.685 0.0041 **
## Residuals 165 8.306 0.05034
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(L_IPS.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = DFR_L_IPS ~ level, data = split_DFR_delay[["all"]])
##
## $level
## diff lwr upr p adj
## med-high -0.06108331 -0.1613662 0.03919954 0.3225542
## low-high -0.14249284 -0.2427757 -0.04220999 0.0027663
## low-med -0.08140953 -0.1816924 0.01887332 0.1362678print("L preSMA")## [1] "L preSMA"L_preSMA.aov <- aov(DFR_L_preSMA ~ level, data=split_DFR_delay[["all"]])
summary(L_preSMA.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.867 0.4334 7.62 0.000684 ***
## Residuals 165 9.385 0.0569
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(L_preSMA.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = DFR_L_preSMA ~ level, data = split_DFR_delay[["all"]])
##
## $level
## diff lwr upr p adj
## med-high -0.04077966 -0.1473775 0.06581818 0.6380645
## low-high -0.16862032 -0.2752182 -0.06202248 0.0007351
## low-med -0.12784066 -0.2344385 -0.02124282 0.0141448print("R dlPFC")## [1] "R dlPFC"R_dlPFC.aov <- aov(DFR_R_dlPFC ~ level, data=split_DFR_delay[["all"]])
summary(R_dlPFC.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.853 0.4264 5.675 0.00414 **
## Residuals 165 12.398 0.0751
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(R_dlPFC.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = DFR_R_dlPFC ~ level, data = split_DFR_delay[["all"]])
##
## $level
## diff lwr upr p adj
## med-high -0.15604359 -0.2785624 -0.03352479 0.0083964
## low-high -0.14570503 -0.2682238 -0.02318623 0.0151450
## low-med 0.01033856 -0.1121802 0.13285736 0.9782852print("R dMFG")## [1] "R dMFG"R_dMFG.aov <- aov(DFR_R_dMFG ~ level, data=split_DFR_delay[["all"]])
summary(R_dMFG.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.70 0.350 5.223 0.00632 **
## Residuals 165 11.06 0.067
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(R_dMFG.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = DFR_R_dMFG ~ level, data = split_DFR_delay[["all"]])
##
## $level
## diff lwr upr p adj
## med-high -0.12285140 -0.2385489 -0.007153935 0.0345290
## low-high -0.14763033 -0.2633278 -0.031932865 0.0082542
## low-med -0.02477893 -0.1404764 0.090918540 0.8682684print("R IPS")## [1] "R IPS"R_IPS.aov <- aov(DFR_R_IPS ~ level, data=split_DFR_delay[["all"]])
summary(R_IPS.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.714 0.3569 4.893 0.00863 **
## Residuals 165 12.035 0.0729
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(R_IPS.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = DFR_R_IPS ~ level, data = split_DFR_delay[["all"]])
##
## $level
## diff lwr upr p adj
## med-high -0.09424707 -0.2149556 0.02646142 0.1578139
## low-high -0.15872652 -0.2794350 -0.03801803 0.0062119
## low-med -0.06447945 -0.1851879 0.05622904 0.4179414print("R medial Parietal")## [1] "R medial Parietal"R_medParietal.aov <- aov(DFR_R_medParietal ~ level, data=split_DFR_delay[["all"]])
summary(R_medParietal.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.56 0.7820 3.956 0.021 *
## Residuals 165 32.61 0.1977
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(R_medParietal.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = DFR_R_medParietal ~ level, data = split_DFR_delay[["all"]])
##
## $level
## diff lwr upr p adj
## med-high -0.22691284 -0.4256253 -0.02820033 0.0207656
## low-high -0.17071269 -0.3694252 0.02799981 0.1078362
## low-med 0.05620015 -0.1425124 0.25491265 0.7818678Scatter Plots
Let’s look at some scatter plots for the more linear looking things - particularly the high load and the load effect.
data_for_plot <- merge(p200_data,p200_indiv_ROI_DFR_delay,by="PTID")
ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=DFR_L_aMFG))+
geom_point()+
stat_smooth(method="lm")+
geom_text(x=0.9,y=1,label="r=0.27***")+
ggtitle("L aMFG")
print("L aMFG")## [1] "L aMFG"cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$DFR_L_aMFG)##
## Pearson's product-moment correlation
##
## data: data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$DFR_L_aMFG
## t = 3.6757, df = 168, p-value = 0.0003188
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1275544 0.4066460
## sample estimates:
## cor
## 0.2728306ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=DFR_L_dlPFC))+
geom_point()+
stat_smooth(method="lm")+
geom_text(x=0.9,y=1,label="r=0.26***")+
ggtitle("L dlPFC")
print("L dlPFC")## [1] "L dlPFC"cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$DFR_L_dlPFC)##
## Pearson's product-moment correlation
##
## data: data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$DFR_L_dlPFC
## t = 3.5387, df = 168, p-value = 0.0005202
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1175198 0.3981079
## sample estimates:
## cor
## 0.2633753ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=DFR_L_dMFG))+
geom_point()+
stat_smooth(method="lm")+
geom_text(x=0.9,y=0.75,label="r=0.16*")+
ggtitle("L dMFG")
print("L dMFG")## [1] "L dMFG"cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$DFR_L_dMFG)##
## Pearson's product-moment correlation
##
## data: data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$DFR_L_dMFG
## t = 2.0918, df = 168, p-value = 0.03796
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.009023415 0.302579639
## sample estimates:
## cor
## 0.1593213ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=DFR_L_IPS))+
geom_point()+
stat_smooth(method="lm")+
geom_text(x=0.9,y=0.75,label="r=0.24**")+
ggtitle("L IPS")
print("L IPS")## [1] "L IPS"cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$DFR_L_IPS)##
## Pearson's product-moment correlation
##
## data: data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$DFR_L_IPS
## t = 3.2651, df = 168, p-value = 0.001326
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.09734466 0.38079319
## sample estimates:
## cor
## 0.2442798ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=DFR_L_preSMA))+
geom_point()+
stat_smooth(method="lm")+
geom_text(x=0.9,y=1,label="r=0.28***")+
ggtitle("L preSMA")
print("L preSMA")## [1] "L preSMA"cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$DFR_L_preSMA)##
## Pearson's product-moment correlation
##
## data: data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$DFR_L_preSMA
## t = 3.7087, df = 168, p-value = 0.0002828
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1299569 0.4086830
## sample estimates:
## cor
## 0.2750902ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=DFR_R_dlPFC))+
geom_point()+
stat_smooth(method="lm")+
geom_text(x=0.9,y=1.25,label="r=0.23**")+
ggtitle("R dlPFC")
print("R dlPFC")## [1] "R dlPFC"cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$DFR_R_dlPFC)##
## Pearson's product-moment correlation
##
## data: data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$DFR_R_dlPFC
## t = 3.1068, df = 168, p-value = 0.002221
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.08557902 0.37060306
## sample estimates:
## cor
## 0.2330909ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=DFR_R_dMFG))+
geom_point()+
stat_smooth(method="lm")+
geom_text(x=0.9,y=1.25,label="r=0.20**")+
ggtitle("R dMFG")
print("R dMFG")## [1] "R dMFG"cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$DFR_R_dMFG)##
## Pearson's product-moment correlation
##
## data: data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$DFR_R_dMFG
## t = 2.7004, df = 168, p-value = 0.007634
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.05514113 0.34391879
## sample estimates:
## cor
## 0.2039626ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=DFR_R_IPS))+
geom_point()+
stat_smooth(method="lm")+
geom_text(x=0.9,y=1.25,label="r=0.25**")+
ggtitle("R IPS")
print("R IPS")## [1] "R IPS"cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$DFR_R_IPS)##
## Pearson's product-moment correlation
##
## data: data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$DFR_R_IPS
## t = 3.3318, df = 168, p-value = 0.001061
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1022765 0.3850442
## sample estimates:
## cor
## 0.2489582ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=DFR_R_medParietal))+
geom_point()+
stat_smooth(method="lm")+
geom_text(x=0.9,y=1.75,label="r=0.18*")+
ggtitle("R medial Parietal")
print("R medial Parietal")## [1] "R medial Parietal"cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$DFR_R_medParietal)##
## Pearson's product-moment correlation
##
## data: data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$DFR_R_medParietal
## t = 2.4465, df = 168, p-value = 0.01546
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.03596677 0.32686610
## sample estimates:
## cor
## 0.1854769Probe Period
Full Mask
No differences in the probe period.
fullMask_delay_plots[["probe_low"]]+fullMask_delay_plots[["probe_high"]]+fullMask_delay_plots[["probe_loadEffect"]]+
plot_annotation(title="BOLD signal from full delay period mask during probe period")
print("Low Load")## [1] "Low Load"probe_L1.aov <- aov(probe_low ~ level, data=split_fullMask_delay[["all"]])
summary(probe_L1.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.64 0.8220 1.793 0.17
## Residuals 165 75.64 0.4584print("High Load")## [1] "High Load"probe_L3.aov <- aov(probe_high ~ level, data=split_fullMask_delay[["all"]])
summary(probe_L3.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 2.79 1.3959 2.013 0.137
## Residuals 165 114.40 0.6933print("Load Effect")## [1] "Load Effect"probe_LE.aov <- aov(probe_loadEffect ~ level, data=split_fullMask_delay[["all"]])
summary(probe_LE.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 2.67 1.3365 2.09 0.127
## Residuals 165 105.51 0.6395data_for_plot <- merge(p200_data,p200_DFR_full_mask,by="PTID")
ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=probe_loadEffect))+
geom_point()+
stat_smooth(method="lm")+
geom_text(x=0.9,y=2.5,label="r=0.19*")+
ggtitle("Load Effect")
print("Load Effect")## [1] "Load Effect"cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$probe_loadEffect)##
## Pearson's product-moment correlation
##
## data: data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$probe_loadEffect
## t = 2.5975, df = 168, p-value = 0.01022
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.0473804 0.3370397
## sample estimates:
## cor
## 0.1964934Individual ROIs
DFR_probe_plots[["dmPFC_loadEffect"]] + DFR_probe_plots[["L_aMFG_loadEffect"]] + DFR_probe_plots[["L_dlPFC_loadEffect"]] +
plot_annotation(title="individual DFR activity from probe period regions")
DFR_probe_plots[["L_insula_loadEffect"]] + DFR_probe_plots[["L_IPS_loadEffect"]] + DFR_probe_plots[["R_dlPFC_loadEffect"]] 
DFR_probe_plots[["R_insula_loadEffect"]] + DFR_probe_plots[["R_OFC_loadEffect"]] 
print("dmPFC")## [1] "dmPFC"probe_dmPFC.aov <- aov(dmPFC_loadEffect ~ level, data=split_DFR_probe[["all"]])
summary(probe_dmPFC.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.12 0.5588 1.894 0.154
## Residuals 165 48.68 0.2950print("L aMFG")## [1] "L aMFG"probe_L_aMFG.aov <- aov(L_aMFG_loadEffect ~ level, data=split_DFR_probe[["all"]])
summary(probe_L_aMFG.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.01 0.0032 0.005 0.995
## Residuals 165 115.38 0.6992print("L dlPFC")## [1] "L dlPFC"probe_L_dlPFC.aov <- aov(L_dlPFC_loadEffect ~ level, data=split_DFR_probe[["all"]])
summary(probe_L_dlPFC.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.97 0.9826 2.253 0.108
## Residuals 165 71.98 0.4362print("L insula")## [1] "L insula"probe_L_insula.aov <- aov(L_insula_loadEffect ~ level, data=split_DFR_probe[["all"]])
summary(probe_L_insula.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.684 0.3421 1.891 0.154
## Residuals 165 29.844 0.1809print("R dlPFC")## [1] "R dlPFC"probe_R_dlPFC.aov <- aov(R_dlPFC_loadEffect ~ level, data=split_DFR_probe[["all"]])
summary(probe_R_dlPFC.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.17 0.0832 0.205 0.815
## Residuals 165 67.11 0.4068print("R Insula")## [1] "R Insula"probe_R_insula.aov <- aov(R_insula_loadEffect ~ level, data=split_DFR_probe[["all"]])
summary(probe_R_insula.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.339 0.1693 0.972 0.381
## Residuals 165 28.748 0.1742print("R OFC")## [1] "R OFC"probe_R_OFC.aov <- aov(R_OFC_loadEffect ~ level, data=split_DFR_probe[["all"]])
summary(probe_R_OFC.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.24 0.1182 0.612 0.544
## Residuals 165 31.88 0.1932FFA
Only see difference in L FFA load effect during cue, with high > low.
DFR_FFA_plots[["L_CUE_LE"]] + DFR_FFA_plots[["L_DELAY_LE"]] + DFR_FFA_plots[["L_PROBE_LE"]]+
plot_annotation(title="FFA during DFR task")
DFR_FFA_plots[["R_CUE_LE"]] + DFR_FFA_plots[["R_DELAY_LE"]] + DFR_FFA_plots[["R_PROBE_LE"]]
print("L Cue")## [1] "L Cue"L_CUE_LE_FFA.aov <- aov(L_CUE_LE ~ level, data=split_DFR_FFA[["all"]])
summary(L_CUE_LE_FFA.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 2.69 1.3460 4.023 0.0197 *
## Residuals 164 54.87 0.3346
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(L_CUE_LE_FFA.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = L_CUE_LE ~ level, data = split_DFR_FFA[["all"]])
##
## $level
## diff lwr upr p adj
## med-high -0.1127546 -0.3724683 0.14695908 0.5610187
## low-high -0.3065647 -0.5651059 -0.04802350 0.0155111
## low-med -0.1938101 -0.4535238 0.06590365 0.1846592print("R Cue")## [1] "R Cue"R_CUE_LE_FFA.aov <- aov(R_CUE_LE ~ level, data=split_DFR_FFA[["all"]])
summary(R_CUE_LE_FFA.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.76 0.8790 2.837 0.0615 .
## Residuals 164 50.81 0.3098
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print("L Delay")## [1] "L Delay"L_DELAY_LE_FFA.aov <- aov(L_DELAY_LE ~ level, data=split_DFR_FFA[["all"]])
summary(L_DELAY_LE_FFA.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.021 0.01036 0.24 0.787
## Residuals 164 7.089 0.04323print("R Delay")## [1] "R Delay"R_DELAY_LE_FFA.aov <- aov(R_DELAY_LE ~ level, data=split_DFR_FFA[["all"]])
summary(R_DELAY_LE_FFA.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.069 0.03459 0.915 0.402
## Residuals 164 6.196 0.03778print("L Probe")## [1] "L Probe"L_PROBE_LE_FFA.aov <- aov(L_PROBE_LE ~ level, data=split_DFR_FFA[["all"]])
summary(L_PROBE_LE_FFA.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 3.59 1.7951 1.838 0.162
## Residuals 164 160.14 0.9765print("R Probe")## [1] "R Probe"R_PROBE_LE_FFA.aov <- aov(R_PROBE_LE ~ level, data=split_DFR_FFA[["all"]])
summary(R_PROBE_LE_FFA.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.89 0.4456 0.726 0.485
## Residuals 164 100.67 0.6138data_for_plot <- merge(p200_data,p200_FFA,by="PTID")
ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=R_CUE_LE))+
geom_point()+
stat_smooth(method="lm")+
geom_text(x=0.9,y=2.25,label="r=0.26***")+
ggtitle("R Cue LE")
print("R cue Load Effect")## [1] "R cue Load Effect"cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$R_CUE_LE)##
## Pearson's product-moment correlation
##
## data: data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$R_CUE_LE
## t = 3.5205, df = 167, p-value = 0.0005553
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1165105 0.3980145
## sample estimates:
## cor
## 0.2628475ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=L_CUE_LE))+
geom_point()+
stat_smooth(method="lm")+
geom_text(x=0.9,y=2.25,label="r=0.29***")+
ggtitle("L Cue LE")
print("L cue Load Effect")## [1] "L cue Load Effect"cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$L_CUE_LE)##
## Pearson's product-moment correlation
##
## data: data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$L_CUE_LE
## t = 4.0284, df = 167, p-value = 8.512e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1535378 0.4292746
## sample estimates:
## cor
## 0.2976HPC
No significant differences in any region of the hippocampus.
Anterior
L3
DFR_HPC_Ant_plots[["L_CUE_L3"]] + DFR_HPC_Ant_plots[["L_DELAY_L3"]] + DFR_HPC_Ant_plots[["L_PROBE_L3"]]+
plot_annotation(title="HPC Ant during DFR task")
DFR_HPC_Ant_plots[["R_CUE_L3"]] + DFR_HPC_Ant_plots[["R_DELAY_L3"]] + DFR_HPC_Ant_plots[["R_PROBE_L3"]]
print("L Cue")## [1] "L Cue"L_CUE_L3_HPC_Ant.aov <- aov(L_CUE_L3 ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(L_CUE_L3_HPC_Ant.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.24 0.1198 0.521 0.595
## Residuals 164 37.69 0.2298print("R Cue")## [1] "R Cue"R_CUE_L3_HPC_Ant.aov <- aov(R_CUE_L3 ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(R_CUE_L3_HPC_Ant.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.34 0.1718 0.778 0.461
## Residuals 164 36.21 0.2208print("L Delay")## [1] "L Delay"L_DELAY_L3_HPC_Ant.aov <- aov(L_DELAY_L3 ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(L_DELAY_L3_HPC_Ant.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.035 0.01748 0.578 0.562
## Residuals 164 4.957 0.03023print("R Delay")## [1] "R Delay"R_DELAY_L3_HPC_Ant.aov <- aov(R_DELAY_L3 ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(R_DELAY_L3_HPC_Ant.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.021 0.01032 0.362 0.697
## Residuals 164 4.679 0.02853print("L Probe")## [1] "L Probe"L_PROBE_L3_HPC_Ant.aov <- aov(L_PROBE_L3 ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(L_PROBE_L3_HPC_Ant.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.31 0.1545 0.302 0.74
## Residuals 164 83.97 0.5120print("R Probe")## [1] "R Probe"R_PROBE_L3_HPC_Ant.aov <- aov(R_PROBE_L3 ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(R_PROBE_L3_HPC_Ant.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.87 0.4354 0.763 0.468
## Residuals 164 93.54 0.5704LE
DFR_HPC_Ant_plots[["L_CUE_LE"]] + DFR_HPC_Ant_plots[["L_DELAY_LE"]] + DFR_HPC_Ant_plots[["L_PROBE_LE"]]+
plot_annotation(title="HPC Ant during DFR task")
DFR_HPC_Ant_plots[["R_CUE_LE"]] + DFR_HPC_Ant_plots[["R_DELAY_LE"]] + DFR_HPC_Ant_plots[["R_PROBE_LE"]]
print("L Cue")## [1] "L Cue"L_CUE_LE_HPC_Ant.aov <- aov(L_CUE_LE ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(L_CUE_LE_HPC_Ant.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.22 0.1107 0.564 0.57
## Residuals 164 32.22 0.1965print("R Cue")## [1] "R Cue"R_CUE_LE_HPC_Ant.aov <- aov(R_CUE_LE ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(R_CUE_LE_HPC_Ant.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.25 0.1241 0.634 0.532
## Residuals 164 32.11 0.1958print("L Delay")## [1] "L Delay"L_DELAY_LE_HPC_Ant.aov <- aov(L_DELAY_LE ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(L_DELAY_LE_HPC_Ant.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.013 0.00674 0.196 0.822
## Residuals 164 5.635 0.03436print("R Delay")## [1] "R Delay"R_DELAY_LE_HPC_Ant.aov <- aov(R_DELAY_LE ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(R_DELAY_LE_HPC_Ant.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.029 0.01427 0.43 0.651
## Residuals 164 5.445 0.03320print("L Probe")## [1] "L Probe"L_PROBE_LE_HPC_Ant.aov <- aov(L_PROBE_LE ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(L_PROBE_LE_HPC_Ant.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.90 0.4505 0.756 0.471
## Residuals 164 97.78 0.5962print("R Probe")## [1] "R Probe"R_PROBE_LE_HPC_Ant.aov <- aov(R_PROBE_LE ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(R_PROBE_LE_HPC_Ant.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.28 0.1418 0.234 0.792
## Residuals 164 99.43 0.6063Medial
L3
DFR_HPC_Med_plots[["L_CUE_L3"]] + DFR_HPC_Med_plots[["L_DELAY_L3"]] + DFR_HPC_Med_plots[["L_PROBE_L3"]]+
plot_annotation(title="HPC_Med during DFR task")
DFR_HPC_Med_plots[["R_CUE_L3"]] + DFR_HPC_Med_plots[["R_DELAY_L3"]] + DFR_HPC_Med_plots[["R_PROBE_L3"]]
print("L Cue")## [1] "L Cue"L_CUE_L3_HPC_Med.aov <- aov(L_CUE_L3 ~ level, data=split_DFR_HPC_Med[["all"]])
summary(L_CUE_L3_HPC_Med.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.11 0.05493 0.324 0.724
## Residuals 164 27.81 0.16959print("R Cue")## [1] "R Cue"R_CUE_L3_HPC_Med.aov <- aov(R_CUE_L3 ~ level, data=split_DFR_HPC_Med[["all"]])
summary(R_CUE_L3_HPC_Med.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.283 0.1417 0.846 0.431
## Residuals 164 27.480 0.1676print("L Delay")## [1] "L Delay"L_DELAY_L3_HPC_Med.aov <- aov(L_DELAY_L3 ~ level, data=split_DFR_HPC_Med[["all"]])
summary(L_DELAY_L3_HPC_Med.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.019 0.009596 0.476 0.622
## Residuals 164 3.307 0.020163print("R Delay")## [1] "R Delay"R_DELAY_L3_HPC_Med.aov <- aov(R_DELAY_L3 ~ level, data=split_DFR_HPC_Med[["all"]])
summary(R_DELAY_L3_HPC_Med.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.045 0.02243 1.154 0.318
## Residuals 164 3.188 0.01944print("L Probe")## [1] "L Probe"L_PROBE_L3_HPC_Med.aov <- aov(L_PROBE_L3 ~ level, data=split_DFR_HPC_Med[["all"]])
summary(L_PROBE_L3_HPC_Med.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.86 0.4318 1.118 0.329
## Residuals 164 63.33 0.3862print("R Probe")## [1] "R Probe"R_PROBE_L3_HPC_Med.aov <- aov(R_PROBE_L3 ~ level, data=split_DFR_HPC_Med[["all"]])
summary(R_PROBE_L3_HPC_Med.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.43 0.7165 1.871 0.157
## Residuals 164 62.80 0.3829LE
DFR_HPC_Med_plots[["L_CUE_LE"]] + DFR_HPC_Med_plots[["L_DELAY_LE"]] + DFR_HPC_Med_plots[["L_PROBE_LE"]]+
plot_annotation(title="HPC_Med during DFR task")
DFR_HPC_Med_plots[["R_CUE_LE"]] + DFR_HPC_Med_plots[["R_DELAY_LE"]] + DFR_HPC_Med_plots[["R_PROBE_LE"]]
print("L Cue")## [1] "L Cue"L_CUE_LE_HPC_Med.aov <- aov(L_CUE_LE ~ level, data=split_DFR_HPC_Med[["all"]])
summary(L_CUE_LE_HPC_Med.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.608 0.3041 2.654 0.0734 .
## Residuals 164 18.789 0.1146
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print("R Cue")## [1] "R Cue"R_CUE_LE_HPC_Med.aov <- aov(R_CUE_LE ~ level, data=split_DFR_HPC_Med[["all"]])
summary(R_CUE_LE_HPC_Med.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.372 0.1862 1.467 0.234
## Residuals 164 20.817 0.1269print("L Delay")## [1] "L Delay"L_DELAY_LE_HPC_Med.aov <- aov(L_DELAY_LE ~ level, data=split_DFR_HPC_Med[["all"]])
summary(L_DELAY_LE_HPC_Med.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.003 0.001608 0.067 0.935
## Residuals 164 3.934 0.023988print("R Delay")## [1] "R Delay"R_DELAY_LE_HPC_Med.aov <- aov(R_DELAY_LE ~ level, data=split_DFR_HPC_Med[["all"]])
summary(R_DELAY_LE_HPC_Med.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.019 0.009294 0.421 0.657
## Residuals 164 3.624 0.022097print("L Probe")## [1] "L Probe"L_PROBE_LE_HPC_Med.aov <- aov(L_PROBE_LE ~ level, data=split_DFR_HPC_Med[["all"]])
summary(L_PROBE_LE_HPC_Med.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.22 0.1103 0.258 0.773
## Residuals 164 70.08 0.4273print("R Probe")## [1] "R Probe"R_PROBE_LE_HPC_Med.aov <- aov(R_PROBE_LE ~ level, data=split_DFR_HPC_Med[["all"]])
summary(R_PROBE_LE_HPC_Med.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.80 0.3977 1.094 0.337
## Residuals 164 59.63 0.3636Posterior
L3
DFR_HPC_Post_plots[["L_CUE_L3"]] + DFR_HPC_Post_plots[["L_DELAY_L3"]] + DFR_HPC_Post_plots[["L_PROBE_L3"]]+
plot_annotation(title="HPC_Post during DFR task")
DFR_HPC_Post_plots[["R_CUE_L3"]] + DFR_HPC_Post_plots[["R_DELAY_L3"]] + DFR_HPC_Post_plots[["R_PROBE_L3"]]
print("L Cue")## [1] "L Cue"L_CUE_L3_HPC_Post.aov <- aov(L_CUE_L3 ~ level, data=split_DFR_HPC_Post[["all"]])
summary(L_CUE_L3_HPC_Post.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.333 0.1666 1.177 0.311
## Residuals 164 23.222 0.1416print("R Cue")## [1] "R Cue"R_CUE_L3_HPC_Post.aov <- aov(R_CUE_L3 ~ level, data=split_DFR_HPC_Post[["all"]])
summary(R_CUE_L3_HPC_Post.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.461 0.2303 1.668 0.192
## Residuals 164 22.645 0.1381print("L Delay")## [1] "L Delay"L_DELAY_L3_HPC_Post.aov <- aov(L_DELAY_L3 ~ level, data=split_DFR_HPC_Post[["all"]])
summary(L_DELAY_L3_HPC_Post.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0423 0.02117 1.582 0.209
## Residuals 164 2.1944 0.01338print("R Delay")## [1] "R Delay"R_DELAY_L3_HPC_Post.aov <- aov(R_DELAY_L3 ~ level, data=split_DFR_HPC_Post[["all"]])
summary(R_DELAY_L3_HPC_Post.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0567 0.02833 2.439 0.0904 .
## Residuals 164 1.9053 0.01162
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print("L Probe")## [1] "L Probe"L_PROBE_L3_HPC_Post.aov <- aov(L_PROBE_L3 ~ level, data=split_DFR_HPC_Post[["all"]])
summary(L_PROBE_L3_HPC_Post.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.38 0.1876 0.635 0.531
## Residuals 164 48.47 0.2955print("R Probe")## [1] "R Probe"R_PROBE_L3_HPC_Post.aov <- aov(R_PROBE_L3 ~ level, data=split_DFR_HPC_Post[["all"]])
summary(R_PROBE_L3_HPC_Post.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.22 0.1085 0.361 0.698
## Residuals 164 49.32 0.3007LE
DFR_HPC_Post_plots[["L_CUE_LE"]] + DFR_HPC_Post_plots[["L_DELAY_LE"]] + DFR_HPC_Post_plots[["L_PROBE_LE"]]+
plot_annotation(title="HPC_Post during DFR task")
DFR_HPC_Post_plots[["R_CUE_LE"]] + DFR_HPC_Post_plots[["R_DELAY_LE"]] + DFR_HPC_Post_plots[["R_PROBE_LE"]]
print("L Cue")## [1] "L Cue"L_CUE_LE_HPC_Post.aov <- aov(L_CUE_LE ~ level, data=split_DFR_HPC_Post[["all"]])
summary(L_CUE_LE_HPC_Post.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.174 0.08689 0.891 0.412
## Residuals 164 15.999 0.09755print("R Cue")## [1] "R Cue"R_CUE_LE_HPC_Post.aov <- aov(R_CUE_LE ~ level, data=split_DFR_HPC_Post[["all"]])
summary(R_CUE_LE_HPC_Post.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.162 0.08123 0.932 0.396
## Residuals 164 14.293 0.08715print("L Delay")## [1] "L Delay"L_DELAY_LE_HPC_Post.aov <- aov(L_DELAY_LE ~ level, data=split_DFR_HPC_Post[["all"]])
summary(L_DELAY_LE_HPC_Post.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0065 0.003273 0.18 0.835
## Residuals 164 2.9776 0.018156print("R Delay")## [1] "R Delay"R_DELAY_LE_HPC_Post.aov <- aov(R_DELAY_LE ~ level, data=split_DFR_HPC_Post[["all"]])
summary(R_DELAY_LE_HPC_Post.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0014 0.00068 0.05 0.951
## Residuals 164 2.2391 0.01365print("L Probe")## [1] "L Probe"L_PROBE_LE_HPC_Post.aov <- aov(L_PROBE_LE ~ level, data=split_DFR_HPC_Post[["all"]])
summary(L_PROBE_LE_HPC_Post.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.02 0.0084 0.025 0.975
## Residuals 164 54.61 0.3330print("R Probe")## [1] "R Probe"R_PROBE_LE_HPC_Post.aov <- aov(R_PROBE_LE ~ level, data=split_DFR_HPC_Post[["all"]])
summary(R_PROBE_LE_HPC_Post.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.38 0.1906 0.643 0.527
## Residuals 164 48.63 0.2965Cortical Thickness
Only see differences in the L probe regions, with med > high.
cortical_thickness_plots[["Cue_RH"]] + cortical_thickness_plots[["Delay_RH"]] + cortical_thickness_plots[["Probe_RH"]] +
plot_annotation(title="Cortical Thickness from DFR Full Mask") 
cortical_thickness_plots[["Cue_LH"]] + cortical_thickness_plots[["Delay_LH"]] + cortical_thickness_plots[["Probe_LH"]]
print("L Cue")## [1] "L Cue"L_CUE_DFR_thick.aov <- aov(Cue_LH ~ level, data=split_cortical_thickness_DFR[["all"]])
summary(L_CUE_DFR_thick.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0131 0.006569 0.739 0.479
## Residuals 164 1.4574 0.008887print("R Cue")## [1] "R Cue"R_CUE_DFR_thick.aov <- aov(Cue_RH ~ level, data=split_cortical_thickness_DFR[["all"]])
summary(R_CUE_LE_HPC_Post.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.162 0.08123 0.932 0.396
## Residuals 164 14.293 0.08715print("L Delay")## [1] "L Delay"L_DELAY_DFR_thick.aov <- aov(Delay_LH ~ level, data=split_cortical_thickness_DFR[["all"]])
summary(L_DELAY_DFR_thick.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.075 0.03748 1.687 0.188
## Residuals 163 3.622 0.02222
## 1 observation deleted due to missingnessprint("R Delay")## [1] "R Delay"R_DELAY_DFR_thick.aov <- aov(Delay_RH ~ level, data=split_cortical_thickness_DFR[["all"]])
summary(R_DELAY_DFR_thick.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0221 0.01106 0.645 0.526
## Residuals 164 2.8152 0.01717print("L Probe")## [1] "L Probe"L_PROBE_DFR_thick.aov <- aov(Probe_LH ~ level, data=split_cortical_thickness_DFR[["all"]])
summary(L_PROBE_DFR_thick.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0506 0.02532 1.38 0.254
## Residuals 164 3.0086 0.01835print("R Probe")## [1] "R Probe"R_PROBE_DFR_thick.aov <- aov(Probe_RH ~ level, data=split_cortical_thickness_DFR[["all"]])
summary(R_PROBE_DFR_thick.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.1159 0.05793 3.279 0.0401 *
## Residuals 164 2.8974 0.01767
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(R_PROBE_DFR_thick.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Probe_RH ~ level, data = split_cortical_thickness_DFR[["all"]])
##
## $level
## diff lwr upr p adj
## med-high 0.06434623 0.004665777 0.12402669 0.0312089
## low-high 0.02676429 -0.032646732 0.08617530 0.5368331
## low-med -0.03758195 -0.097262405 0.02209851 0.2986389Resting State Functional Connectivity
Within Network
Differences within FPCN (med > high) and VAN (med and low > high).
RS_plots[["FPCN_FPCN"]] + RS_plots[["DMN_DMN"]] + RS_plots[["DAN_DAN"]]+
plot_annotation(title="Resting State Functional Connectivity - Within Networks")
RS_plots[["VAN_VAN"]] + RS_plots[["CO_CO"]] + RS_plots[["visual_visual"]]
print("FPCN")## [1] "FPCN"FPCN.aov <- aov(FPCN_FPCN ~ level, data=split_RS[["all"]])
summary(FPCN.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0516 0.025801 3.545 0.0311 *
## Residuals 164 1.1936 0.007278
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(FPCN.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = FPCN_FPCN ~ level, data = split_RS[["all"]])
##
## $level
## diff lwr upr p adj
## med-high 0.04303510 0.004729856 0.08134034 0.0234141
## low-high 0.02371511 -0.014417200 0.06184741 0.3075572
## low-med -0.01931999 -0.057625235 0.01898525 0.4590698print("DMN")## [1] "DMN"DMN.aov <- aov(DMN_DMN ~ level, data=split_RS[["all"]])
summary(DMN.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0476 0.023814 2.538 0.0821 .
## Residuals 164 1.5387 0.009382
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print("DAN")## [1] "DAN"DAN.aov <- aov(DAN_DAN ~ level, data=split_RS[["all"]])
summary(DAN.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0099 0.004967 0.634 0.532
## Residuals 164 1.2843 0.007831print("VAN")## [1] "VAN"VAN.aov <- aov(VAN_VAN ~ level, data=split_RS[["all"]])
summary(VAN.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.1191 0.05955 7.196 0.00101 **
## Residuals 164 1.3572 0.00828
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(VAN.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = VAN_VAN ~ level, data = split_RS[["all"]])
##
## $level
## diff lwr upr p adj
## med-high 0.04593331 0.005087387 0.08677924 0.0232524
## low-high 0.06309417 0.022432651 0.10375569 0.0009513
## low-med 0.01716086 -0.023685067 0.05800678 0.5818832print("CO")## [1] "CO"CO.aov <- aov(CO_CO ~ level, data=split_RS[["all"]])
summary(CO.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0233 0.011627 1.444 0.239
## Residuals 164 1.3205 0.008052print("CO")## [1] "CO"visual.aov <- aov(visual_visual ~ level, data=split_RS[["all"]])
summary(visual.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0086 0.004293 0.249 0.78
## Residuals 164 2.8272 0.017239Across Network
No across RS network differences.
RS_plots[["FPCN_DMN"]] + RS_plots[["FPCN_DAN"]] + RS_plots[["FCPN_VAN"]]+
plot_annotation(title="Resting State Functional Connectivity - Across Networks")
RS_plots[["FPCN_CO"]] + RS_plots[["FPCN_visual"]]
print("FPCN DMN")## [1] "FPCN DMN"FPCN_DMN.aov <- aov(FPCN_DMN ~ level, data=split_RS[["all"]])
summary(FPCN_DMN.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0194 0.009688 1.196 0.305
## Residuals 164 1.3289 0.008103print("FPCN DAN")## [1] "FPCN DAN"FPCN_DAN.aov <- aov(FPCN_DAN ~ level, data=split_RS[["all"]])
summary(FPCN_DAN.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0129 0.006428 1.254 0.288
## Residuals 164 0.8405 0.005125print("FPCN VAN")## [1] "FPCN VAN"FPCN_VAN.aov <- aov(FPCN_VAN ~ level, data=split_RS[["all"]])
summary(FPCN_VAN.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0064 0.003187 0.459 0.633
## Residuals 164 1.1396 0.006949print("FPCN CO")## [1] "FPCN CO"FPCN_CO.aov <- aov(FPCN_CO ~ level, data=split_RS[["all"]])
summary(FPCN_CO.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0115 0.005748 0.556 0.574
## Residuals 164 1.6946 0.010333print("FPCN visual")## [1] "FPCN visual"FPCN_visual.aov <- aov(FPCN_visual ~ level, data=split_RS[["all"]])
summary(FPCN_visual.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0004 0.000186 0.025 0.976
## Residuals 164 1.2309 0.007506Beta Series Connectivity
Cue Period
High Load
Differences in the beta series connectivity for FPCN/HPC connectivity (med > high), FPCN/FFA (med and low > high), and HPC/FFA (med > high)
beta_conn_cue_plots[["FPCN_FPCN_L3"]] + beta_conn_cue_plots[["FPCN_HPC_L3"]] +
plot_annotation(title = "Beta Series Connectivity at High Load")
beta_conn_cue_plots[["FPCN_FFA_L3"]] + beta_conn_cue_plots[["HPC_FFA_L3"]]
FPCN_FPCN_BC_cue_L3.aov <- aov(FPCN_FPCN_L3 ~ level, data = split_beta_conn_cue[["all"]])
summary(FPCN_FPCN_BC_cue_L3.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.214 0.1070 0.821 0.442
## Residuals 164 21.371 0.1303FPCN_HPC_BC_cue_L3.aov <- aov(FPCN_HPC_L3 ~ level, data = split_beta_conn_cue[["all"]])
summary(FPCN_HPC_BC_cue_L3.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.871 0.4355 4.173 0.0171 *
## Residuals 164 17.115 0.1044
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(FPCN_HPC_BC_cue_L3.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = FPCN_HPC_L3 ~ level, data = split_beta_conn_cue[["all"]])
##
## $level
## diff lwr upr p adj
## med-high 0.17589316 0.03084184 0.32094447 0.0129087
## low-high 0.10547445 -0.03892200 0.24987090 0.1979807
## low-med -0.07041871 -0.21547002 0.07463261 0.4859175FPCN_FFA_BC_cue_L3.aov <- aov(FPCN_FFA_L3 ~ level, data = split_beta_conn_cue[["all"]])
summary(FPCN_FFA_BC_cue_L3.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.304 0.6521 6.415 0.00208 **
## Residuals 164 16.672 0.1017
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(FPCN_FFA_BC_cue_L3.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = FPCN_FFA_L3 ~ level, data = split_beta_conn_cue[["all"]])
##
## $level
## diff lwr upr p adj
## med-high 0.16024635 0.01708777 0.3034049 0.0240437
## low-high 0.20548974 0.06297748 0.3480020 0.0023437
## low-med 0.04524339 -0.09791520 0.1884020 0.7355717HPC_FFA_BC_cue_L3.aov <- aov(HPC_FFA_L3 ~ level, data = split_beta_conn_cue[["all"]])
summary(HPC_FFA_BC_cue_L3.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.691 0.3457 3.558 0.0307 *
## Residuals 164 15.932 0.0971
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(HPC_FFA_BC_cue_L3.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = HPC_FFA_L3 ~ level, data = split_beta_conn_cue[["all"]])
##
## $level
## diff lwr upr p adj
## med-high 0.1423694 0.002421533 0.2823173 0.0451697
## low-high 0.1293965 -0.009919540 0.2687126 0.0747075
## low-med -0.0129729 -0.152920794 0.1269750 0.9738527Scatter Plots
data_for_plot <- merge(p200_data,p200_beta_conn_cue,by="PTID")
ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=FPCN_FFA_L3))+
geom_point()+
stat_smooth(method="lm")+
geom_text(x=0.9,y=2,label="r=-0.22**")+
ggtitle("FPCN/FFA L3")
print("FPCN/FFA L3")## [1] "FPCN/FFA L3"cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$FPCN_FFA_L3)##
## Pearson's product-moment correlation
##
## data: data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$FPCN_FFA_L3
## t = -2.8628, df = 167, p-value = 0.004738
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.35563295 -0.06752927
## sample estimates:
## cor
## -0.2162844Load Effect
For the load effects, however, we only see FPCN/FPCN (med > high) and FPCN/FFA (low > high).
beta_conn_cue_plots[["FPCN_FPCN_LE"]] + beta_conn_cue_plots[["FPCN_HPC_LE"]] +
plot_annotation(title = "Beta Series Connectivity Load Effect")
beta_conn_cue_plots[["FPCN_FFA_LE"]] + beta_conn_cue_plots[["HPC_FFA_LE"]]
FPCN_FPCN_BC_cue_LE.aov <- aov(FPCN_FPCN_LE ~ level, data = split_beta_conn_cue[["all"]])
summary(FPCN_FPCN_BC_cue_LE.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.024 0.5122 3.384 0.0363 *
## Residuals 164 24.825 0.1514
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(FPCN_FPCN_BC_cue_LE.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = FPCN_FPCN_LE ~ level, data = split_beta_conn_cue[["all"]])
##
## $level
## diff lwr upr p adj
## med-high 0.17248805 -0.002204571 0.3471807 0.0537927
## low-high 0.15857460 -0.015329335 0.3324785 0.0818754
## low-med -0.01391345 -0.188606066 0.1607792 0.9806297FPCN_HPC_BC_cue_LE.aov <- aov(FPCN_HPC_LE ~ level, data = split_beta_conn_cue[["all"]])
summary(FPCN_HPC_BC_cue_LE.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.315 0.1575 1.411 0.247
## Residuals 164 18.313 0.1117FPCN_FFA_BC_cue_LE.aov <- aov(FPCN_FFA_LE ~ level, data = split_beta_conn_cue[["all"]])
summary(FPCN_FFA_BC_cue_LE.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.132 0.5659 3.298 0.0394 *
## Residuals 164 28.141 0.1716
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(FPCN_FFA_BC_cue_LE.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = FPCN_FFA_LE ~ level, data = split_beta_conn_cue[["all"]])
##
## $level
## diff lwr upr p adj
## med-high 0.15990313 -0.0260913353 0.3458976 0.1074873
## low-high 0.18575564 0.0006008839 0.3709104 0.0490621
## low-med 0.02585251 -0.1601419506 0.2118470 0.9421874HPC_FFA_BC_cue_LE.aov <- aov(HPC_FFA_LE ~ level, data = split_beta_conn_cue[["all"]])
summary(HPC_FFA_BC_cue_LE.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.10 0.04998 0.314 0.731
## Residuals 164 26.15 0.15943Delay Period
No differences for the beta series connectivity during delay period.
High Load
beta_conn_delay_plots[["FPCN_FPCN_L3"]] + beta_conn_delay_plots[["FPCN_HPC_L3"]] +
plot_annotation(title = "Beta Series Connectivity at High Load")
beta_conn_delay_plots[["FPCN_FFA_L3"]] + beta_conn_delay_plots[["HPC_FFA_L3"]]
FPCN_FPCN_BC_delay_L3.aov <- aov(FPCN_FPCN_L3 ~ level, data = split_beta_conn_delay[["all"]])
summary(FPCN_FPCN_BC_delay_L3.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.178 0.08922 0.854 0.428
## Residuals 164 17.132 0.10446FPCN_HPC_BC_delay_L3.aov <- aov(FPCN_HPC_L3 ~ level, data = split_beta_conn_delay[["all"]])
summary(FPCN_HPC_BC_delay_L3.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.276 0.13786 1.91 0.151
## Residuals 164 11.837 0.07218FPCN_FFA_BC_delay_L3.aov <- aov(FPCN_FFA_L3 ~ level, data = split_beta_conn_delay[["all"]])
summary(FPCN_FFA_BC_delay_L3.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.098 0.04920 0.837 0.435
## Residuals 164 9.645 0.05881HPC_FFA_BC_delay_L3.aov <- aov(HPC_FFA_L3 ~ level, data = split_beta_conn_delay[["all"]])
summary(HPC_FFA_BC_delay_L3.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.29 0.14514 2.294 0.104
## Residuals 164 10.38 0.06327Scatter plots
data_for_plot <- merge(p200_data,p200_beta_conn_delay,by="PTID")
ggplot(data = data_for_plot, aes(x=XDFR_MRI_ACC_L3,y=FPCN_HPC_L3))+
geom_point()+
stat_smooth(method="lm")+
geom_text(x=0.9,y=1.25,label="r=-0.18*")+
ggtitle("FPCN/HPC L3")
print("FPCN/HPC L3")## [1] "FPCN/HPC L3"cor.test(data_for_plot$XDFR_MRI_ACC_L3,data_for_plot$FPCN_HPC_L3)##
## Pearson's product-moment correlation
##
## data: data_for_plot$XDFR_MRI_ACC_L3 and data_for_plot$FPCN_HPC_L3
## t = -2.3698, df = 167, p-value = 0.01894
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.32254842 -0.03023422
## sample estimates:
## cor
## -0.180371Load Effect
beta_conn_delay_plots[["FPCN_FPCN_LE"]] + beta_conn_delay_plots[["FPCN_HPC_LE"]] +
plot_annotation(title = "Beta Series Connectivity Load Effect")
beta_conn_delay_plots[["FPCN_FFA_LE"]] + beta_conn_delay_plots[["HPC_FFA_LE"]]
FPCN_FPCN_BC_delay_LE.aov <- aov(FPCN_FPCN_LE ~ level, data = split_beta_conn_delay[["all"]])
summary(FPCN_FPCN_BC_delay_LE.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.018 0.00904 0.192 0.825
## Residuals 164 7.714 0.04704FPCN_HPC_BC_delay_LE.aov <- aov(FPCN_HPC_LE ~ level, data = split_beta_conn_delay[["all"]])
summary(FPCN_HPC_BC_delay_LE.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.063 0.03153 0.501 0.607
## Residuals 164 10.319 0.06292FPCN_FFA_BC_delay_LE.aov <- aov(FPCN_FFA_LE ~ level, data = split_beta_conn_delay[["all"]])
summary(FPCN_FFA_BC_delay_LE.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.181 0.09059 1.305 0.274
## Residuals 164 11.384 0.06942HPC_FFA_BC_delay_LE.aov <- aov(HPC_FFA_LE ~ level, data = split_beta_conn_delay[["all"]])
summary(HPC_FFA_BC_delay_LE.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.225 0.1126 1.279 0.281
## Residuals 164 14.433 0.0880TukeyHSD(HPC_FFA_BC_delay_LE.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = HPC_FFA_LE ~ level, data = split_beta_conn_delay[["all"]])
##
## $level
## diff lwr upr p adj
## med-high -0.05287245 -0.1860714 0.08032648 0.6165419
## low-high -0.08915921 -0.2217568 0.04343837 0.2526714
## low-med -0.03628675 -0.1694857 0.09691218 0.7958256BCT Measures
No differences in any of the BCT measures.
Overall Measures
BCT_plots[["Participation_Coef_Mean"]] + BCT_plots[["Global_Eff"]] + BCT_plots[["Modularity_Louvain_N"]]+
plot_annotation(title="Overall BCT Measures")
print("Mean Participation Coefficient")## [1] "Mean Participation Coefficient"partic_coef_mean.aov <- aov(Participation_Coef_Mean ~ level, data = split_BCT[["all"]])
summary(partic_coef_mean.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.04 0.01992 0.277 0.759
## Residuals 164 11.81 0.07199print("Global Efficiency")## [1] "Global Efficiency"global_eff.aov <- aov(Global_Eff ~ level, data = split_BCT[["all"]])
summary(global_eff.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.0139 0.006959 0.945 0.391
## Residuals 164 1.2074 0.007362print("Modularity")## [1] "Modularity"modularity.aov <- aov(Modularity_Louvain_N ~ level, data = split_BCT[["all"]])
summary(modularity.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 3.1 1.525 0.211 0.81
## Residuals 164 1186.7 7.236Individual Networks
indiv_partic_coeff_plots[["FrontoParietal"]] + indiv_partic_coeff_plots[["Default"]] + indiv_partic_coeff_plots[["DorsalAttn"]]+
plot_annotation(title="Individual Network Participation Coefficient")
indiv_partic_coeff_plots[["CinguloOperc"]] + indiv_partic_coeff_plots[["VentralAttn"]] + indiv_partic_coeff_plots[["Visual"]]
print("FPCN")## [1] "FPCN"FPCN_indiv_coeff.aov <- aov(FrontoParietal ~ level, data = split_indiv_partic_coeff[["all"]])
summary(FPCN_indiv_coeff.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.631 0.3153 1.779 0.172
## Residuals 164 29.067 0.1772print("DMN")## [1] "DMN"DMN_indiv_coeff.aov <- aov(Default ~ level, data = split_indiv_partic_coeff[["all"]])
summary(DMN_indiv_coeff.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.035 0.01756 0.097 0.908
## Residuals 164 29.821 0.18183print("DAN")## [1] "DAN"DAN_indiv_coeff.aov <- aov(DorsalAttn ~ level, data = split_indiv_partic_coeff[["all"]])
summary(DAN_indiv_coeff.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.234 0.11718 1.376 0.256
## Residuals 164 13.969 0.08517print("CO")## [1] "CO"CO_indiv_coeff.aov <- aov(CinguloOperc ~ level, data = split_indiv_partic_coeff[["all"]])
summary(CO_indiv_coeff.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.30 0.1500 1.27 0.284
## Residuals 164 19.37 0.1181print("VAN")## [1] "VAN"VAN_indiv_coeff.aov <- aov(VentralAttn ~ level, data = split_indiv_partic_coeff[["all"]])
summary(VAN_indiv_coeff.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.065 0.03235 0.28 0.756
## Residuals 164 18.981 0.11574print("visual")## [1] "visual"visual_indiv_coeff.aov <- aov(Visual ~ level, data = split_indiv_partic_coeff[["all"]])
summary(visual_indiv_coeff.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.086 0.04307 0.244 0.783
## Residuals 164 28.893 0.17618